Source 1primary paper2021
Toolkit/dCas9-based gene networks
dCas9-based gene networks
Taxonomy: Mechanism Branch / Architecture. Workflows sit above the mechanism and technique branches rather than replacing them.
Summary
dCas9-based gene networks are a construct pattern used in combination with a synthetic demultiplexer to build pulsatile-signal filters and decoders within synthetic dynamic signal-processing circuits. In the cited 2021 study, these networks contributed to decoding complex temporal inputs into differential gene expression outputs.
Usefulness & Problems
Why this is useful
This construct pattern is useful for implementing dynamic signal decoding in synthetic gene circuits, including filtering and decoding pulsatile inputs. The cited work also states that such systems support precise multidimensional regulation of a heterologous metabolic pathway and may enable biotechnological applications.
Source:
Finally, we use dynamic multiplexing for precise multidimensional regulation of a heterologous metabolic pathway.
Source:
show that this circuit can be employed to demultiplex dynamically encoded signals
Problem solved
It addresses the problem of converting complex temporal input signals into distinct gene expression outputs in engineered cells. Specifically, the evidence supports its use in constructing pulsatile-signal filters and decoders when combined with a demultiplexing module.
Source:
Finally, we use dynamic multiplexing for precise multidimensional regulation of a heterologous metabolic pathway.
Taxonomy & Function
Primary hierarchy
Mechanism Branch
Architecture: A reusable architecture pattern for arranging parts into an engineered system.
Techniques
No technique tags yet.
Target processes
No target processes tagged yet.
Implementation Constraints
cofactor dependency: cofactor requirement unknownencoding mode: genetically encodedimplementation constraint: context specific validationoperating role: sensor
Implementation involved combining a synthetic demultiplexer with dCas9-based gene networks in a dynamic signal-processing framework. The evidence does not provide practical details such as guide RNA design, transcriptional effector domains, delivery method, cofactors, or expression system.
The supplied evidence does not specify the exact dCas9 effector architecture, target promoters, host organism, or quantitative performance metrics. Independent replication is not provided in the evidence, and validation appears limited to the reported study context.
Validation
Cell-freeBacteriaMammalianMouseHumanTherapeuticIndep. Replication
Supporting Sources
Ranked Claims
Dynamic multiplexing was used for precise multidimensional regulation of a heterologous metabolic pathway.
Finally, we use dynamic multiplexing for precise multidimensional regulation of a heterologous metabolic pathway.
Dynamic multiplexing was used for precise multidimensional regulation of a heterologous metabolic pathway.
Finally, we use dynamic multiplexing for precise multidimensional regulation of a heterologous metabolic pathway.
Dynamic multiplexing was used for precise multidimensional regulation of a heterologous metabolic pathway.
Finally, we use dynamic multiplexing for precise multidimensional regulation of a heterologous metabolic pathway.
Dynamic multiplexing was used for precise multidimensional regulation of a heterologous metabolic pathway.
Finally, we use dynamic multiplexing for precise multidimensional regulation of a heterologous metabolic pathway.
Dynamic multiplexing was used for precise multidimensional regulation of a heterologous metabolic pathway.
Finally, we use dynamic multiplexing for precise multidimensional regulation of a heterologous metabolic pathway.
Dynamic multiplexing was used for precise multidimensional regulation of a heterologous metabolic pathway.
Finally, we use dynamic multiplexing for precise multidimensional regulation of a heterologous metabolic pathway.
Dynamic multiplexing was used for precise multidimensional regulation of a heterologous metabolic pathway.
Finally, we use dynamic multiplexing for precise multidimensional regulation of a heterologous metabolic pathway.
The reported systems elucidate design principles of dynamic information processing and provide synthetic systems capable of decoding complex signals for biotechnological applications.
Our results elucidate design principles of dynamic information processing and provide original synthetic systems capable of decoding complex signals for biotechnological applications.
The reported systems elucidate design principles of dynamic information processing and provide synthetic systems capable of decoding complex signals for biotechnological applications.
Our results elucidate design principles of dynamic information processing and provide original synthetic systems capable of decoding complex signals for biotechnological applications.
The reported systems elucidate design principles of dynamic information processing and provide synthetic systems capable of decoding complex signals for biotechnological applications.
Our results elucidate design principles of dynamic information processing and provide original synthetic systems capable of decoding complex signals for biotechnological applications.
The reported systems elucidate design principles of dynamic information processing and provide synthetic systems capable of decoding complex signals for biotechnological applications.
Our results elucidate design principles of dynamic information processing and provide original synthetic systems capable of decoding complex signals for biotechnological applications.
The reported systems elucidate design principles of dynamic information processing and provide synthetic systems capable of decoding complex signals for biotechnological applications.
Our results elucidate design principles of dynamic information processing and provide original synthetic systems capable of decoding complex signals for biotechnological applications.
The reported systems elucidate design principles of dynamic information processing and provide synthetic systems capable of decoding complex signals for biotechnological applications.
Our results elucidate design principles of dynamic information processing and provide original synthetic systems capable of decoding complex signals for biotechnological applications.
The reported systems elucidate design principles of dynamic information processing and provide synthetic systems capable of decoding complex signals for biotechnological applications.
Our results elucidate design principles of dynamic information processing and provide original synthetic systems capable of decoding complex signals for biotechnological applications.
Combining the demultiplexer with dCas9-based gene networks enabled construction of pulsatile-signal filters and decoders.
We combine this demultiplexer with dCas9-based gene networks to construct pulsatile-signal filters and decoders.
Combining the demultiplexer with dCas9-based gene networks enabled construction of pulsatile-signal filters and decoders.
We combine this demultiplexer with dCas9-based gene networks to construct pulsatile-signal filters and decoders.
Combining the demultiplexer with dCas9-based gene networks enabled construction of pulsatile-signal filters and decoders.
We combine this demultiplexer with dCas9-based gene networks to construct pulsatile-signal filters and decoders.
Combining the demultiplexer with dCas9-based gene networks enabled construction of pulsatile-signal filters and decoders.
We combine this demultiplexer with dCas9-based gene networks to construct pulsatile-signal filters and decoders.
Combining the demultiplexer with dCas9-based gene networks enabled construction of pulsatile-signal filters and decoders.
We combine this demultiplexer with dCas9-based gene networks to construct pulsatile-signal filters and decoders.
Combining the demultiplexer with dCas9-based gene networks enabled construction of pulsatile-signal filters and decoders.
We combine this demultiplexer with dCas9-based gene networks to construct pulsatile-signal filters and decoders.
Combining the demultiplexer with dCas9-based gene networks enabled construction of pulsatile-signal filters and decoders.
We combine this demultiplexer with dCas9-based gene networks to construct pulsatile-signal filters and decoders.
Light-responsive transcriptional regulators with differing response kinetics were used to build a falling-edge pulse-detector.
Exploiting light-responsive transcriptional regulators with differing response kinetics, we build a falling-edge pulse-detector
Light-responsive transcriptional regulators with differing response kinetics were used to build a falling-edge pulse-detector.
Exploiting light-responsive transcriptional regulators with differing response kinetics, we build a falling-edge pulse-detector
Light-responsive transcriptional regulators with differing response kinetics were used to build a falling-edge pulse-detector.
Exploiting light-responsive transcriptional regulators with differing response kinetics, we build a falling-edge pulse-detector
Light-responsive transcriptional regulators with differing response kinetics were used to build a falling-edge pulse-detector.
Exploiting light-responsive transcriptional regulators with differing response kinetics, we build a falling-edge pulse-detector
Light-responsive transcriptional regulators with differing response kinetics were used to build a falling-edge pulse-detector.
Exploiting light-responsive transcriptional regulators with differing response kinetics, we build a falling-edge pulse-detector
Light-responsive transcriptional regulators with differing response kinetics were used to build a falling-edge pulse-detector.
Exploiting light-responsive transcriptional regulators with differing response kinetics, we build a falling-edge pulse-detector
Light-responsive transcriptional regulators with differing response kinetics were used to build a falling-edge pulse-detector.
Exploiting light-responsive transcriptional regulators with differing response kinetics, we build a falling-edge pulse-detector
The falling-edge pulse-detector can be employed to demultiplex dynamically encoded signals.
show that this circuit can be employed to demultiplex dynamically encoded signals
The falling-edge pulse-detector can be employed to demultiplex dynamically encoded signals.
show that this circuit can be employed to demultiplex dynamically encoded signals
The falling-edge pulse-detector can be employed to demultiplex dynamically encoded signals.
show that this circuit can be employed to demultiplex dynamically encoded signals
The falling-edge pulse-detector can be employed to demultiplex dynamically encoded signals.
show that this circuit can be employed to demultiplex dynamically encoded signals
The falling-edge pulse-detector can be employed to demultiplex dynamically encoded signals.
show that this circuit can be employed to demultiplex dynamically encoded signals
The falling-edge pulse-detector can be employed to demultiplex dynamically encoded signals.
show that this circuit can be employed to demultiplex dynamically encoded signals
The falling-edge pulse-detector can be employed to demultiplex dynamically encoded signals.
show that this circuit can be employed to demultiplex dynamically encoded signals
Dynamic multiplexing significantly increases the information transmission capacity from signal to gene expression state.
Applying information theory, we show that dynamic multiplexing significantly increases the information transmission capacity from signal to gene expression state.
Dynamic multiplexing significantly increases the information transmission capacity from signal to gene expression state.
Applying information theory, we show that dynamic multiplexing significantly increases the information transmission capacity from signal to gene expression state.
Dynamic multiplexing significantly increases the information transmission capacity from signal to gene expression state.
Applying information theory, we show that dynamic multiplexing significantly increases the information transmission capacity from signal to gene expression state.
Dynamic multiplexing significantly increases the information transmission capacity from signal to gene expression state.
Applying information theory, we show that dynamic multiplexing significantly increases the information transmission capacity from signal to gene expression state.
Dynamic multiplexing significantly increases the information transmission capacity from signal to gene expression state.
Applying information theory, we show that dynamic multiplexing significantly increases the information transmission capacity from signal to gene expression state.
Dynamic multiplexing significantly increases the information transmission capacity from signal to gene expression state.
Applying information theory, we show that dynamic multiplexing significantly increases the information transmission capacity from signal to gene expression state.
Dynamic multiplexing significantly increases the information transmission capacity from signal to gene expression state.
Applying information theory, we show that dynamic multiplexing significantly increases the information transmission capacity from signal to gene expression state.
Approval Evidence
1 source1 linked approval claimfirst-pass slug dcas9-based-gene-networks
We combine this demultiplexer with dCas9-based gene networks
Source:
engineering resultsupports
Combining the demultiplexer with dCas9-based gene networks enabled construction of pulsatile-signal filters and decoders.
We combine this demultiplexer with dCas9-based gene networks to construct pulsatile-signal filters and decoders.
Source:
Comparisons
Source-backed strengths
The reported system enabled construction of pulsatile-signal filters and decoders by combining a demultiplexer with dCas9-based gene networks. The broader study further demonstrated dynamic multiplexing for precise multidimensional regulation of a heterologous metabolic pathway and articulated design principles for dynamic information processing.
Source:
We combine this demultiplexer with dCas9-based gene networks to construct pulsatile-signal filters and decoders.
Source:
Exploiting light-responsive transcriptional regulators with differing response kinetics, we build a falling-edge pulse-detector
Source:
Applying information theory, we show that dynamic multiplexing significantly increases the information transmission capacity from signal to gene expression state.
Compared with demultiplexer for dynamically encoded signals
dCas9-based gene networks and demultiplexer for dynamically encoded signals address a similar problem space.
Shared frame: same top-level item type; shared mechanisms: dynamic signal decoding
Compared with falling-edge pulse-detector
dCas9-based gene networks and falling-edge pulse-detector address a similar problem space.
Shared frame: same top-level item type; shared mechanisms: dynamic signal decoding
Strengths here: looks easier to implement in practice.
Compared with pulsatile-signal filters and decoders
dCas9-based gene networks and pulsatile-signal filters and decoders address a similar problem space.
Shared frame: same top-level item type; shared mechanisms: dynamic signal decoding
Ranked Citations
- 1.